Asset Details
Do you wish to reserve the book?
Study of an effective machine learning-integrated science curriculum for high school youth in an informal learning setting
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Study of an effective machine learning-integrated science curriculum for high school youth in an informal learning setting
Do you wish to request the book?
Study of an effective machine learning-integrated science curriculum for high school youth in an informal learning setting
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Study of an effective machine learning-integrated science curriculum for high school youth in an informal learning setting
2025
Overview
Purpose
This study evaluates the effectiveness of a machine learning (ML) integrated science curriculum implemented within the Science Research Mentorship Program (SRMP) for high school youth at the American Museum of Natural History (AMNH) over 2 years. The 4-week curriculum focused on ML knowledge gain, skill development, and self-efficacy, particularly for under-represented youth in STEM.
Background
ML is increasingly prevalent in STEM fields, making early exposure to ML methods and artificial intelligence (AI) literacy crucial for youth pursuing STEM careers. However, STEM fields, particularly those focused on AI research and development, suffer from a lack of diversity. Learning experiences that support the participation of under-represented groups in STEM and ML are essential to addressing this gap.
Results
Participant learning was assessed through pre- and post-surveys measuring ML knowledge, skills, and self-efficacy. Results from the implementation of the curriculum show that participants gained understanding of ML knowledge and skills (
p
< 0.001,
d
= 1.083) and self-efficacy in learning ML concepts (
p
= 0.004,
d
= 0.676). On average, participants who identified as female and non-white showed greater learning gains than their white male peers (ML knowledge:
p
< 0.001,
d
= 1.191; self-efficacy:
p
= 0.006,
d
= 0.631), decreasing gaps in ML knowledge, skills, and self-efficacy identified in pre-survey scores.
Conclusions
The ML-integrated curriculum effectively enhances students’ understanding and confidence in ML concepts, especially for under-represented groups in STEM, and provides a model for future ML education initiatives in informal science settings. We suggest that policy makers and school leaders take into account that high school age youth can learn ML concepts through integrated curricula while maintaining an awareness that curriculum effectiveness varies across demographic groups.
Publisher
Springer International Publishing,Springer Nature B.V,SpringerOpen
Subject
